Econometric analysis

For first-year graduate courses in Econometrics for Social Scientists. Bridging the gap between social science studies and econometric analysis Designed to bridge the gap between social science studies and field-econometrics, Econometric Analysis, 8th Edition, Global Edition, presents this ever-grow...

Descripción completa

Detalles Bibliográficos
Otros Autores:	Greene, William H., 1951- author (author)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Harlow, England : Pearson [2020]
Edición:	Eighth, Global edition
Materias:	Econometrics. Libros electrónicos.
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009632535506719

Tabla de Contenidos:

Cover
Title Page
Copyright Page
Brief Contents
Contents
Examples and Applications
Preface
Part I: The Linear Regression Model
CHAPTER 1 Econometrics
1.1 Introduction
1.2 The Paradigm of Econometrics
1.3 The Practice of Econometrics
1.4 Microeconometrics and Macroeconometrics
1.5 Econometric Modeling
1.6 Plan of the Book
1.7 Preliminaries
1.7.1 Numerical Examples
1.7.2 Software and Replication
1.7.3 Notational Conventions
CHAPTER 2 The Linear Regression Model
2.1 Introduction
2.2 The Linear Regression Model
2.3 Assumptions of the Linear Regression Model
2.3.1 Linearity of the Regression Model
2.3.2 Full Rank
2.3.3 Regression
2.3.4 Homoscedastic and Nonautocorrelated Disturbances
2.3.5 Data Generating Process for the Regressors
2.3.6 Normality
2.3.7 Independence and Exogeneity
2.4 Summary and Conclusions
CHAPTER 3 Least Squares Regression
3.1 Introduction
3.2 Least Squares Regression
3.2.1 The Least Squares Coefficient Vector
3.2.2 Application: An Investment Equation
3.2.3 Algebraic Aspects of the Least Squares Solution
3.2.4 Projection
3.3 Partitioned Regression and Partial Regression
3.4 Partial Regression and Partial Correlation Coefficients
3.5 Goodness of Fit and the Analysis of Variance
3.5.1 The Adjusted R-Squared and a Measure of Fit
3.5.2 R-Squared and the Constant Term in the Model
3.5.3 Comparing Models
3.6 Linearly Transformed Regression
3.7 Summary and Conclusions
CHAPTER 4 Estimating the Regression Model by Least Squares
4.1 Introduction
4.2 Motivating Least Squares
4.2.1 Population Orthogonality Conditions
4.2.2 Minimum Mean Squared Error Predictor
4.2.3 Minimum Variance Linear Unbiased Estimation
4.3 Statistical Properties of the Least Squares Estimator.
4.3.1 Unbiased Estimation
4.3.2 Omitted Variable Bias
4.3.3 Inclusion of Irrelevant Variables
4.3.4 Variance of the Least Squares Estimator
4.3.5 The Gauss-Markov Theorem
4.3.6 The Normality Assumption
4.4 Asymptotic Properties of the Least Squares Estimator
4.4.1 Consistency of the Least Squares Estimator of ß
4.4.2 The Estimator of Asy. Var[b]
4.4.3 Asymptotic Normality of the Least Squares Estimator
4.4.4 Asymptotic Efficiency
4.4.5 Linear Projections
4.5 Robust Estimation and Inference
4.5.1 Consistency of the Least Squares Estimator
4.5.2 A Heteroscedasticity Robust Covariance Matrix for Least Squares
4.5.3 Robustness to Clustering
4.5.4 Bootstrapped Standard Errors with Clustered Data
4.6 Asymptotic Distribution of a Function of b: The Delta Method
4.7 Interval Estimation
4.7.1 Forming a Confidence Interval for a Coefficient
4.7.2 Confidence Interval for a Linear Combination of Coefficients: the Oaxaca Decomposition
4.8 Prediction and Forecasting
4.8.1 Prediction Intervals
4.8.2 Predicting y when the Regression Model Describes Log y
4.8.3 Prediction Interval for y when the Regression Model Describes Log y
4.8.4 Forecasting
4.9 Data Problems
4.9.1 Multicollinearity
4.9.2 Principal Components
4.9.3 Missing Values and Data Imputation
4.9.4 Measurement Error
4.9.5 Outliers and Influential Observations
4.10 Summary and Conclusions
CHAPTER 5 Hypothesis Tests and Model Selection
5.1 Introduction
5.2 Hypothesis Testing Methodology
5.2.1 Restrictions and Hypotheses
5.2.2 Nested Models
5.2.3 Testing Procedures
5.2.4 Size, Power, and Consistency of a Test
5.2.5 A Methodological Dilemma: Bayesian Versus Classical Testing
5.3 Three Approaches to Testing Hypotheses
5.3.1 Wald Tests Based on the Distance Measure.
5.3.1.a Testing a Hypothesis About a Coefficient
5.3.1.b The F Statistic
5.3.2 Tests Based on the Fit of the Regression
5.3.2.a The Restricted Least Squares Estimator
5.3.2.b The Loss of Fit from Restricted Least Squares
5.3.2.c Testing the Significance of the Regression
5.3.2.d Solving Out the Restrictions and a Caution about R2
5.3.3 Lagrange Multiplier Tests
5.4 Large-Sample Tests and Robust Inference
5.5 Testing Nonlinear Restrictions
5.6 Choosing Between Nonnested Models
5.6.1 Testing Nonnested Hypotheses
5.6.2 An Encompassing Model
5.6.3 Comprehensive Approach-The J Test
5.7 A Specification Test
5.8 Model Building-A General to Simple Strategy
5.8.1 Model Selection Criteria
5.8.2 Model Selection
5.8.3 Classical Model Selection
5.8.4 Bayesian Model Averaging
5.9 Summary and Conclusions
CHAPTER 6 Functional Form, Difference in Differences, and Structural Change
6.1 Introduction
6.2 Using Binary Variables
6.2.1 Binary Variables in Regression
6.2.2 Several Categories
6.2.3 Modeling Individual Heterogeneity
6.2.4 Sets of Categories
6.2.5 Threshold Effects and Categorical Variables
6.2.6 Transition Tables
6.3 Difference in Differences Regression
6.3.1 Treatment Effects
6.3.2 Examining the Effects of Discrete Policy Changes
6.4 Using Regression Kinks and Discontinuities to Analyze Social Policy
6.4.1 Regression Kinked Design
6.4.2 Regression Discontinuity Design
6.5 Nonlinearity in the Variables
6.5.1 Functional Forms
6.5.2 Interaction Effects
6.5.3 Identifying Nonlinearity
6.5.4 Intrinsically Linear Models
6.6 Structural Break and Parameter Variation
6.6.1 Different Parameter Vectors
6.6.2 Robust Tests of Structural Break with Unequal Variances
6.6.3 Pooling Regressions
6.7 Summary And Conclusions.
CHAPTER 7 Nonlinear, Semiparametric, and Nonparametric Regression Models
7.1 Introduction
7.2 Nonlinear Regression Models
7.2.1 Assumptions of the Nonlinear Regression Model
7.2.2 The Nonlinear Least Squares Estimator
7.2.3 Large-Sample Properties of the Nonlinear Least Squares Estimator
7.2.4 Robust Covariance Matrix Estimation
7.2.5 Hypothesis Testing and Parametric Restrictions
7.2.6 Applications
7.2.7 Loglinear Models
7.2.8 Computing the Nonlinear Least Squares Estimator
7.3 Median and Quantile Regression
7.3.1 Least Absolute Deviations Estimation
7.3.2 Quantile Regression Models
7.4 Partially Linear Regression
7.5 Nonparametric Regression
7.6 Summary and Conclusions
CHAPTER 8 Endogeneity and Instrumental Variable Estimation
8.1 Introduction
8.2 Assumptions of the Extended Model
8.3 Instrumental Variables Estimation
8.3.1 Least Squares
8.3.2 The Instrumental Variables Estimator
8.3.3 Estimating the Asymptotic Covariance Matrix
8.3.4 Motivating the Instrumental Variables Estimator
8.4 Two-Stage Least Squares, Control Functions, and Limited Information Maximum Likelihood
8.4.1 Two-Stage Least Squares
8.4.2 A Control Function Approach
8.4.3 Limited Information Maximum Likelihood
8.5 Endogenous Dummy Variables: Estimating Treatment Effects
8.5.1 Regression Analysis of Treatment Effects
8.5.2 Instrumental Variables
8.5.3 A Control Function Estimator
8.5.4 Propensity Score Matching
8.6 Hypothesis Tests
8.6.1 Testing Restrictions
8.6.2 Specification Tests
8.6.3 Testing for Endogeneity: The Hausman and Wu Specification Tests
8.6.4 A Test for Overidentification
8.7 Weak Instruments and LIML
8.8 Measurement Error
8.8.1 Least Squares Attenuation
8.8.2 Instrumental Variables Estimation
8.8.3 Proxy Variables.
8.9 Nonlinear Instrumental Variables Estimation
8.10 Natural Experiments and the Search for Causal Effects
8.11 Summary and Conclusions
Part II: Generalized Regression Model and Equation Systems
CHAPTER 9 The Generalized Regression Model and Heteroscedasticity
9.1 Introduction
9.2 Robust Least Squares Estimation and Inference
9.3 Properties of Least Squares and Instrumental Variables
9.3.1 Finite-Sample Properties of Least Squares
9.3.2 Asymptotic Properties of Least Squares
9.3.3 Heteroscedasticity and Var[b|X]
9.3.4 Instrumental Variable Estimation
9.4 Efficient Estimation by Generalized Least Squares
9.4.1 Generalized Least Squares (GLS)
9.4.2 Feasible Generalized Least Squares (FGLS)
9.5 Heteroscedasticity and Weighted Least Squares
9.5.1 Weighted Least Squares
9.5.2 Weighted Least Squares with Known Ω
9.5.3 Estimation When Ω Contains Unknown Parameters
9.6 Testing for Heteroscedasticity
9.6.1 White's General Test
9.6.2 The Lagrange Multiplier Test
9.7 Two Applications
9.7.1 Multiplicative Heteroscedasticity
9.7.2 Groupwise Heteroscedasticity
9.8 Summary and Conclusions
CHAPTER 10 Systems of Regression Equations
10.1 Introduction
10.2 The Seemingly Unrelated Regressions Model
10.2.1 Ordinary Least Squares And Robust Inference
10.2.2 Generalized Least Squares
10.2.3 Feasible Generalized Least Squares
10.2.4 Testing Hypotheses
10.2.5 The Pooled Model
10.3 Systems of Demand Equations: Singular Systems
10.3.1 Cobb-Douglas Cost Function
10.3.2 Flexible Functional Forms: The Translog Cost Function
10.4 Simultaneous Equations Models
10.4.1 Systems of Equations
10.4.2 A General Notation for Linear Simultaneous Equations Models
10.4.3 The Identification Problem
10.4.4 Single Equation Estimation and Inference.
10.4.5 System Methods of Estimation.

Econometric analysis

Ejemplares similares